Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.

• Publication year

  2018

• Venue

  Journal of the American Statistical Association

• Publication date

  2018-08-13

• Fields of study

  Mathematics, Computer Science, Economics

• Identifiers
  DOI 10.1080/01621459.2020.1751646arXiv 1808.04416
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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